Machine translation is a field of computational linguistics that investigates the use of software to translate text from one natural language to another. At the core of the technology, machine translation substitutes words in one natural language for words in another language. However, this alone cannot produce an understandable translation of text because recognition of whole phrases and their closest counterparts in the target language are needed. Machine translation has improved to translate words, phrases, and sentence structure through gathering data for both the source language and the target language and using this data to generate more linguistically accurate translations.
There are two major types of machine translation amongst many other types. The first major type of machine translation is rule-based machine translation, which uses a combination of language and grammar rules plus dictionaries for common words to translate the corpora from one language to another. A corpora is the plural version of corpus which is a collection of written texts, especially the entire works of a particular author or a body of writing on a particular subject. The other major type of machine translation is statistical machine translation; this type of machine translation has no knowledge of language rules. Instead, statistical machine translation “learns” to translate by analyzing large amounts of data for the source and target language including and not limited to, bilingual and monolingual text. Both major types of machine translation produce similar results; however, statistical machine translation delivers more fluent sounding translation, but at the cost of being less consistent with the translations.